Raspberry Pi 5 Continual Learning Research Platform — Benchmark EWC Against Fine-Tuning

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

This kit transforms a Raspberry Pi 5 into a dedicated testbed for investigating catastrophic forgetting in neural networks. You’ll implement Elastic Weight Consolidation (EWC) and benchmark its performance against naive fine-tuning across sequential image classification tasks, all on a low-power device that mimics the constraints of real-world edge deployment.

What You'll Build

You’ll assemble a compact, autonomous research node with a Pi 5 and high-speed NVMe storage. The setup runs PyTorch-based continual learning experiments directly on the device, allowing you to measure accuracy drops, log Fisher information matrices, and reproduce EWC’s forgetting mitigation in a reproducible environment — no cloud GPU dependency needed.

What You'll Learn

• Integrate NVMe SSD via the M.2 HAT+ and configure fast storage to eliminate I/O bottlenecks during model training
• Implement the Elastic Weight Consolidation loss function, including on-the-fly computation of Fisher information from previous task data
• Design a sequential task benchmark, measuring accuracy degradation after fine-tuning and quantifying the EWC gain
• Set up a headless, always-on machine learning environment on Pi 5 with PyTorch, torchvision, and monitoring tools for long-duration runs

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Designed for B.Tech ECE and EEE final-year students tackling continual learning capstones, Smart India Hackathon participants building edge AI prototypes, and researchers at IIT, NIT, VIT, or BITS Pilani who need an isolated test platform for EWC vs fine-tuning benchmarks. If you’re exploring federated learning with non-IID data on resource-constrained nodes, this kit gives you a reproducible hardware baseline.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.